119 строки
4.0 KiB
Python
119 строки
4.0 KiB
Python
'''Pre-activation ResNet in PyTorch.
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Reference:
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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Identity Mappings in Deep Residual Networks. arXiv:1603.05027
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class PreActBlock(nn.Module):
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'''Pre-activation version of the BasicBlock.'''
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(PreActBlock, self).__init__()
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self.bn1 = nn.BatchNorm2d(in_planes)
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
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if stride != 1 or in_planes != self.expansion*planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
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)
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def forward(self, x):
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out = F.relu(self.bn1(x))
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shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
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out = self.conv1(out)
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out = self.conv2(F.relu(self.bn2(out)))
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out += shortcut
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return out
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class PreActBottleneck(nn.Module):
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'''Pre-activation version of the original Bottleneck module.'''
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expansion = 4
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def __init__(self, in_planes, planes, stride=1):
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super(PreActBottleneck, self).__init__()
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self.bn1 = nn.BatchNorm2d(in_planes)
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
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if stride != 1 or in_planes != self.expansion*planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
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)
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def forward(self, x):
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out = F.relu(self.bn1(x))
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shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
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out = self.conv1(out)
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out = self.conv2(F.relu(self.bn2(out)))
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out = self.conv3(F.relu(self.bn3(out)))
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out += shortcut
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return out
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class PreActResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10):
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super(PreActResNet, self).__init__()
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear = nn.Linear(512*block.expansion, num_classes)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1]*(num_blocks-1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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out = self.conv1(x)
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = F.avg_pool2d(out, 4)
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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def PreActResNet18():
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return PreActResNet(PreActBlock, [2,2,2,2])
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def PreActResNet34():
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return PreActResNet(PreActBlock, [3,4,6,3])
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def PreActResNet50():
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return PreActResNet(PreActBottleneck, [3,4,6,3])
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def PreActResNet101():
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return PreActResNet(PreActBottleneck, [3,4,23,3])
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def PreActResNet152():
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return PreActResNet(PreActBottleneck, [3,8,36,3])
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def test():
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net = PreActResNet18()
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y = net((torch.randn(1,3,32,32)))
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print(y.size())
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# test()
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